Delete app.py

app.py

@@ -1,213 +0,0 @@
-import os
-
-from typing import List
-
-
-
-from langchain.embeddings.openai import OpenAIEmbeddings
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-from langchain.text_splitter import RecursiveCharacterTextSplitter
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-from langchain.vectorstores import Chroma
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-from langchain.chains import (
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-    ConversationalRetrievalChain,
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-)
-
-from langchain.chat_models import ChatOpenAI
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-
-
-from langchain.docstore.document import Document
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-from langchain.memory import ChatMessageHistory, ConversationBufferMemory
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-
-
-import chainlit as cl
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-
-
-os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
-
-
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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-
-
-
-
-@cl.on_chat_start
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-async def on_chat_start():
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-    files = None
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-
-
-    # Wait for the user to upload a file
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-    while files == None:
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-        files = await cl.AskFileMessage(
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-            content="Please upload a text file to begin!",
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-            accept=["text/plain"],
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-            max_size_mb=20,
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-            timeout=180,
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-        ).send()
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-
-
-    file = files[0]
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-
-
-    msg = cl.Message(content=f"Processing `{file.name}`...")
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-    await msg.send()
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-
-
-    with open(file.path, "r", encoding="utf-8") as f:
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-        text = f.read()
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-
-
-    # Split the text into chunks
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-    texts = text_splitter.split_text(text)
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-
-
-    # Create a metadata for each chunk
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-    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
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-
-
-    # Create a Chroma vector store
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-    embeddings = OpenAIEmbeddings()
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-    docsearch = await cl.make_async(Chroma.from_texts)(
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-        texts, embeddings, metadatas=metadatas
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-    )
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-
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-    message_history = ChatMessageHistory()
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-
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-    memory = ConversationBufferMemory(
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-        memory_key="chat_history",
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-        output_key="answer",
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-        chat_memory=message_history,
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-        return_messages=True,
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-    )
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-
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-    # Create a chain that uses the Chroma vector store
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-    chain = ConversationalRetrievalChain.from_llm(
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-        ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
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-        chain_type="stuff",
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-        retriever=docsearch.as_retriever(),
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-        memory=memory,
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-        return_source_documents=True,
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-    )
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-
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-    # Let the user know that the system is ready
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-    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
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-    await msg.update()
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-
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-    cl.user_session.set("chain", chain)
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-
-
-
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-@cl.on_message
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-async def main(message: cl.Message):
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-    chain = cl.user_session.get("chain")  # type: ConversationalRetrievalChain
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-    cb = cl.AsyncLangchainCallbackHandler()
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-
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-    res = await chain.acall(message.content, callbacks=[cb])
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-    answer = res["answer"]
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-    source_documents = res["source_documents"]  # type: List[Document]
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-
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-    text_elements = []  # type: List[cl.Text]
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-
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-    if source_documents:
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-        for source_idx, source_doc in enumerate(source_documents):
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-            source_name = f"source_{source_idx}"
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-            # Create the text element referenced in the message
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-            text_elements.append(
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-                cl.Text(content=source_doc.page_content, name=source_name, display="side")
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-            )
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-        source_names = [text_el.name for text_el in text_elements]
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-
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-        if source_names:
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-            answer += f"\nSources: {', '.join(source_names)}"
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-        else:
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-            answer += "\nNo sources found"
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-
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-    await cl.Message(content=answer, elements=text_elements).send()
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-
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-